Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis


  • A. Ahmad Department of Industrial Computing, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia
  • S. Quegan School of Mathematics and Statistics, University of Sheffield, United Kingdom


Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA) of Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4), three thermal bands (29, 31 and 32), the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.


cloud masking, spectral analysis, principal components analysis, reflectance, brightness temperature


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How to Cite

A. Ahmad and S. Quegan, “Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis”, Eng. Technol. Appl. Sci. Res., vol. 2, no. 3, pp. 221–225, Jun. 2012.


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